1
MUTHAYAMMAL ENGINEERING COLLEGE
(An Autonomous Institution)
(Approved by AICTE, New Delhi, Accredited by NAAC & Affiliated to Anna University)
Rasipuram - 637 408, Namakkal Dist., Tamil Nadu
Course Code & Course Name
:
19ITC17 & Artificial intelligence
Name of the Faculty
:
P.Bhuvaneshwari
Year/Sem/Sec : III/V/-
UNIT- I: INTRODUCTION TO AI AND PRODUCTION SYSTEMS
Term
Notation
Symbol)
Concept/Definition/Meaning/Units/Equation/Exp
ression
Units
AI
Artificial Intelligence is the study of how to make
computers do things which at the moment people do
better.
AI Problem
A problem is really a collection of information that the
agent will use to decide what to do.
Problem
formulation
Problem formulation is the process of deciding what
actions and states to consider for a goal that has been
developed in the first step of problem solving.
Four
components
of a problem
1.An initial state.
2.Actions.
3.Goal test.
4. Path cost.
Production
system
The process of solving the problem can usefully be
modeled as a production system.
BFS
BFS means breath wise search
DFS
DFS means depth wise search
Heuritstic
function
A heuritstic function is a function that maps from
problem state description to measures of desirability,
represented as numbers
Monotonic
production
system
A monotonic production system is a system on which
the application of a rule never prevents the later
application of another rule that could also have been
applied at that time the first rule was selected
Commutative
production
system
A commutative production system is a production
system that is both monotonic and partially
commutative
Hill climbing
Hill climbing is a variant of generate and test in which
MKC
2021-2022
MUST KNOW CONCEPTS
IT
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the feedback from the test procedure is used to help
the generator decide which direction to move in the
search space
CSP
A constraint satisfaction problem is a special kind of
problem satisfies some additional structural properties
beyond the basic requirements for problem in
general.The states are defined by the values of a set of
variables and the goal test specifies a set of constraint
that the value must obey.
Applications
of AI
i. Autonomous planning and scheduling
ii. Game playing
iii. Autonomous control
iv. Diagnosis
v. Logistics planning
vi. Robotics
Search
strategy
Criteria’s
a.Completeness
b.Time complexity
c. Space complexity
d.Optimality
Constraint
graph
It is helpful to visualize the Constraint Satisfaction
Problem as a Constraint Graph. A Consraint Graph is a
graph where the nodes of the graph corresponds to
variables of the problem and the arcs corresponds to
constraints
List of
uninformed
search
techniques
i. Breadth-First Search(BFS).
ii. Depth-First Search(DFS).
iii. Uniform Cost Search.
iv. Depth Limited Search.
v. Iterative Deepening Search.
vi. Bidirectional Search
Generality” in
AI
Generality is the measure of ease with which the
method can be adapted to different domains of
application
A* algorithm
A* algorithm is based on best first search method, as it
gives an idea of optimization and quick choose of
path, and all characteristics lie in A* algorithm
Agent
An agent is anything that can be viewed as perceiving
its environment through Sensors and acting upon the
environment through effectors.
Depth
Limited
Search
Depth-limited avoids the pitfalls of DFS by imposing a
cut off of the maximum depth of a path. This cutoff
can be implemented by special depth limited search
algorithm or by using the general search algorithm
with operators that keep track of the depth
Iterative
deepening
Iterative deepening is a strategy that sidesteps the issue
of choosing the best depth limit by trying all possible
depth limits: first depth 0, then depth 1, then depth 2&
so on
List down the
characteristics
of intelligent
agent
Learning/reasoning Autonomy Goal oriented
Local
maximum
Local maximum is a peak that is higher than each of
its neighboring states but lower than the global
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maximum
State-space
search
technique
State space search involves the use of a graph to keep
track of the relationships between states. Each node of
the graph represents a state of the problem.
Bidirectional
search
The idea behind bidirectional search is to
simultaneously search both forward from the initial
state & backward from the goal & stop when the two
searches meet in the middle.
UNIT- II:- REPRESENTATION OF KNOWLEDGE
26.
First-order
logic
The first-order logic is sufficiently expressive to
represent a good deal of our recommonsense
knowledge. It also either subsumes or forms the
foundation of many other representation languages
27.
symbol
The basic syntactic elements of first-order logic are the
symbols. It stands for objects, relations and functions
28.
Types of
Quantifiers
Universal Quantifiers.
Existential Quantifiers
29.
Logic
A formal language which is used to represent the
knowledge in which
reasoning is carried out to achieve the goal state
30.
Sentence
Each individual representation of facts is called a
sentence. The sentences are
expressed in a language called as knowledge
representation language
31.
Proof
A sequence of application of inference rules is called a
proof. Finding proof
is exactly finding solution to search problems. If the
successor function is defined to generate all possible
applications of inference rules then the search
algorithms can be applied to find proofs
32.
Syntax
Syntax is the arrangement of words. Syntax of
knowledge describes the
possible configurations that can constitute sentences.
Syntax of the language
describes how to make sentences
33.
Semantics
The semantics of the language defines the truth of each
sentence with respect
to each possible world. With this semantics, when a
particular configuration exists within an agent, the
agent believes the corresponding sentence
34.
Modus
Ponen’s rule
in
Propositional
logic
The standard patterns of inference that can be applied
to derive chains of
conclusions that lead to the desired goal is said to be
Modus Ponen’s rule.
35.
Knowledge
base
Knowledge base is the central component of
knowledge base agent and it is
described as a set of representations of facts about the
world
36.
WFF
The Well Formed Formula (WFF) is a sentence in
which all their variables
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are properly introduced with the quantifier in the
beginning of the sentence itself, is called WFF
37.
Horn Clause
Horn clause is a clause with at most one positive literal
in the clause.
Example : P ∨ ⌐Q
38.
KR
Knowledge representation (KR) is an area of artificial
intelligence research
aimed at representing knowledge in symbols to
facilitate inferencing from those knowledge elements,
creating new elements of knowledge
39.
Unification
A matching procedure that compares two literals and
discovers whether there exists a set of substitutions
that makes them identical. This straightforward
procedure is called Unification
40.
Resolution
Resolution is a procedure that gains its efficiency from
the fact that it operates on statements that have been
converted to a very convenient standard form.
Resolution produces proofs by refutation
41.
Skolemizatio
n
Process of removing Universal quantifier is called as
skolemization
42.
Components
of aPredicate
logic
Terms, Predicates and Quantifiers are the components
of Predicate Logic
43.
types of
Matching
Indexing
Matching with variables
Complex and Approximate Matching
Conflict Resolution
44.
forward
chaining
Forward chaining or data-driven inference works by
repeatedly: starting from the current state, matching
the premises of the rules (the IF parts), and performing
the corresponding actions (the THEN parts) that
usually update the knowledge base or working
memory.
45.
Backward
chaining
Backward chaining or goal-driven inference works
towards a final state by looking at the working
memory to see if the sub-goal states already exist
there. If not, the actions (the THEN parts) of the rules
that will establish the sub-goals are identified, and new
sub-goals are set up for achieving the premises of
those rules (the IF parts)
46.
CF
A certainty factor (CF) is a numerical value that
expresses a degree of subjective belief that a particular
item is true. The item may be a fact or a rule. When
probabilities are used attention must be paid to the
underlying assumptions and probability distributions
in order to show validity. Bayes’ rule can be used to
combine probability measures
47.
Fuzzy
Reasoning
Fuzzy Reasoning is based on the theory of fuzzy sets
and it encompasses Artificial Intelligence, information
processing and theories from logic to pure and
Applied mathematics, like graph theory, topology and
optimization
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48.
Frame-based
representation
A frame-based representation facility contributes to a
knowledge system’s ability to reason and can assist the
system designer in determining strategies for
controlling the system’s reasoning.
49.
Truth
preserving
An inference algorithm that derives only entailed
sentences is called sound or truth preserving .
50.
Interpretation
Interpretation specifies exactly which objects, relations
and functions are reffered to by the constant predicate,
and function symbols
UNIT -III : MACHINE LEARNING
51.
Machine
learning
Machine learning is a branch of computer
science which deals with system programming in
order to automatically learn and improve with
experience. For example: Robots are programed
so that they can perform the task based on data
they gather from sensors. It automatically learns
programs from data. 2
52.
Overfitting’ in
Machine
learning
In machine learning, when a statistical model
describes random error or noise instead of
underlying relationship ‘overfitting’ occurs.
When a model is excessively complex,
overfitting is normally observed, because of
having too many parameters with respect to the
number of training data types. The model
exhibits poor performance which has been
overfit
53.
Inductive
machine
learning
The inductive machine learning involves the
process of learning by examples, where a
system, from a set of observed instances tries to
induce a general rule.
54.
five popular
algorithms of
Machine
Learning
a)Decision Trees
b) Neural Networks (back propagation)
c) Probabilistic networks
d) Nearest Neighbor
e) Support vector machines
55.
Algorithm
techniques in
Machine
Learning
a) Supervised Learning b) Unsupervised
Learning c) Semi-supervised Learning d)
Reinforcement Learning e) Transduction f)
Learning to Learn
56.
Standard
approach to
supervised
learning
The standard approach to supervised learning is
to split the set of example into the training set
and the test
57.
Training set’
and ‘Test set’
In various areas of information science like
machine learning, a set of data is used to
discover the potentially predictive relationship
known as ‘Training Set’. Training set is an
examples given to the learner, while Test set is
used to test the accuracy of the hypotheses
generated by the learner, and it is the set of
example held back from the learner. Training set
are distinct from Test set.
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58.
Function of
‘Unsupervised
Learning’
a) Find clusters of the data
b) Find low-dimensional representations of the
data
c) Find interesting directions in data
d) Interesting coordinates and correlations
e) Find novel observations/ database cleaning
59.
Supervised
Learning’
a) Classifications
b) Speech recognition
c) Regression
d) Predict time series
e) Annotate strings
60.
Inductive
Logic
Programming
Inductive Logic Programming (ILP) is a subfield
of machine learning which uses logical
programming representing background
knowledge and examples.
61.
Ensemble
learning
Ensemble learning is used when you build
component classifiers that are more accurate and
independent from each other
62.
PCA
Principal Components Analysis.
63.
KPCA
Kernel based Principal Component Analysis
64.
ICA
Independent Component Analysis are important
feature extraction techniques used for
dimensionality reduction
65.
Support vector
machine
Support vector machines are supervised learning
algorithms used for classification and regression
analysis.
66.
PAC
Probably Approximately Correct- learning is a
learning framework that has been introduced to
analyze learning algorithms and their statistical
efficiency
67.
Sequence
learning
Sequence learning is a method of teaching and
learning in a logical manner
68.
Major
techniques of
Machine
Learning
The two techniques of Machine Learning are a)
Genetic Programming
b) Inductive Learning
69.
Fuzzification
and
Defuzzificatio
n
Fuzzification It is the conversion of crisp
quantity into fuzzy quantity. Defuzzification It is
the conversion of a fuzzy quantity into crisp
quantity.
70.
ANN
Artificial Neural Network is information
processing devices with the capability of
performing computations similar to human brain
or biological neural network.
71.
Applications
of neural
networks
Used in medical field Used in telephone
communication Business applications
72.
BPN
Back Propagation Network (BPN). It is a multi-
layer forward network used extend gradient-
descent waste delta learning rule
73.
ADALINE
ADALINE is Adaptive Linear Neural Element.
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74.
Feedback
networks
feedback networks, which can return back the
output to the input, thereby giving rise to an
iteration process, are defined as feedback
networks.
75.
List the
operations on
fuzzy relations
Union μRUS (x,y)=max(μR(x,y), μS(x,y))
Intersection μR_S (x,y)=min(μR(x,y), μS(x,y))
Complement μR(x,y)= 1- μR(x,y)
Containment R S=>μR(x,y) _μS(x,y)
UNIT- IV: GENETIC ALGORITHMS
Genetic
Algorithms
Genetic Algorithms(GAs) are adaptive
heuristic search algorithms that belong to the
larger part of evolutionary algorithms. Genetic
algorithms are based on the ideas of natural
selection and genetics
Operators of
Genetic
Algorithms
1. Selection Operator
2. Crossover Operator
3. Mutation Operator:
Why use
Genetic
Algorithms
They are Robust
Provide optimisation over large space state.
Unlike traditional AI, they do not break on
slight change in input or presence of noise
Application
of Genetic
Algorithms
Recurrent Neural Network
Mutation testing
Code breaking
Filtering and signal processing
Learning fuzzy rule base etc
Genetic
programming
Genetic programming is one of the two
techniques used in machine learning. The model
is based on the testing and selecting the best
choice among a set of results.
Holland's
schema
theorem
Holland's schema theorem, also called
the fundamental theorem of genetic
algorithms,[1] is an inequality that results from
coarse-graining an equation for evolutionary
dynamics
A cognitive
map
A cognitive map is a mental picture or image of
the layout of one's physical environment
Visual
perception
Visual perception is the ability to see, organize,
and interpret one's environment.
popular of
GA factors
1.Concept is easy to understand
2.Modular
3.Good for “noisy” environments
4.Always an answer; answer gets better with
time
5. State easily distributed
6.Inherently parallel;
Selection
methods in
GA
1. Roulette Wheel selection,
2. Boltzman selection,
3. Tournament selection
4. Rank selection
5. Staedy state selection.
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Advantages
of GA
1.Many ways to speed
2.Easy to exploit previous or alternate solutions
3.Flexible building blocks for hybrid
applications
4.Substantial history and range of use
limitations of
GA
The limitations of genetic programming lie in the
huge search space the GAs have to search for -
an infinite number of equations.
TABU
search
Tabu search is a local search method used for
mathematical optimization. Local searches take a
potential solution to a problem and check its
immediate neighbors in the hope of finding an
improved solution
Application
of Ant
Colony
Search
1 Scheduling problem
2 Vehicle routing problem
3 Assignment problem4 Set problem
Associative
memory
A neural net that is trained to associate a set of
input vectors with a corresponding set of output
vectors is called associative memory.
Why
Hopfield
network is
called as
recurrent
neural
network?
Hopfield nets serve as content-addressable
memory systems with binary threshold nodes.
They are guaranteed to converge to a local
minimum, but convergence to a false pattern
(wrong local minimum) rather than the stored
pattern (expected local minimum) can occur.
Hopfield networks also provide a model for
understanding human memory.
stability-
plasticity
dilemma
A learning agent should be plastic, or adaptive
in reacting to changing environments mean while
it should be stable to preserve knowledge
acquired previously.
Properties of
adaptive
resonance
theory
An ART system has four basic properties. The
first is the self-scaling computational units. The
attentional subsystem is based on competitive
learning enhancing pattern features but
suppressing noise. The second is self-adjusting
memory search. The system can search memory
in parallel and adaptively change its search
order. Third, already learned patterns directly
access their corresponding category. Finally, the
system can adaptively modulate attentional
vigilance using the environment as a teacher.
Simulated
annealing
Simulated annealing (SA) is a generic
probabilistic meta heuristic for the global
optimization problem of locating a good
approximation to the global optimum of a given
function in a large search space. It is often used
when the search space is discrete
Computation
al
neuroscience
Computational neuroscience describes the
nervous system through computational models.
Although this research program is grounded in
mathematical modeling of individual neurons,
the distinctive focus of computational
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neuroscience is systems of interconnected
neurons.
Computation
al theory
Computational theory is the device is
characterized as a mapping from one kind of
information to another, the abstract properties of
this mapping are defined precisely, and its
appropriateness and adequacy for the task as
hand are demonstrated
Cross-over
operation
Cross over is the recombination operation where
two strings and a cross over site are selected to
swap the to produce next set of solutions.
Steps of
Tabu search
1. Neighborhood structure/Move mechanism, 2.
Move Attribute (used for Tabu classification)3.
Tabu status and duration (tenure) 4.Aspiration
criteria5. Stopping criteria
FLC
FLC is Fuzzy logic Controller where a rule base
is formed by an expert for agiven problem and a
suitable solut arrived at using an inferen ion is ce
engine.
System
optimization
This cane defined as a broad set of interrelated
decisions on obtaining, operating, and
maintaining physical and human resources for
electricity generation, transmission, and
distribution that minimize the total cost of
providing electric power to all classes of
consumers, subject to engineering, market, and
regulatory constraints.
UNIT-V: EXPERT SYSTEMS
101.
Expert
System or
Knowledge
Based System
Expert system is a computer system that
emulates the decision-making ability of a human
expert. Expert systems are designed to solve
complex problems by reasoning about
knowledge, represented primarily as if–then
rules rather than through conventional
procedural code.
102.
Inference
engine
Inference engine that consists of algorithms for
manipulating the knowledge represented in the
knowledge base to solve a problem presented to
the system
103.
Roles of
Expert system
Domain expert :The individuals who currently
are experts in solving the problems; here the
system is intended to solve;
Knowledge engineer :The individual who
encodes the expert's knowledge in a declarative
form that can be used by the expert system;
User : The individual who will be consulting
with the system to get advice which would have
been provided by the expert.
104.
Expert
System Shells
Expert systems are built with products called
expert system shells. A shell is a piece of
software which contains the user interface, a
format for declarative knowledge in the
knowledge base, and an inference engine. The
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knowledge and system engineers uses these
shells in making expert systems
105.
Expert
System
Characteristics
1.Operates as an interactive system
2. Tools have ability to sift (filter) knowledge
3. Make logical inferences based on knowledge
stored
4. Ability to Explain Reasoning
5. Domain-Specific
6. Capability to assign Confidence Values
7. Cost-Effective alternative to Human Expert
106.
Application
of Expert
Systems
Diagnosis and Troubleshooting of
Devices and Systems
Planning and Scheduling
Configuration of Manufactured Objects
from sub-assemblies
Financial Decision Making
Knowledge Publishing
Process Monitoring and Control
Design and Manufacturing
107.
List major
components
of an ES
*Knowledge base-the software that represents
the knowledge.
* Inference engine-the reasoning mechanism.
* User interface-the hardware and software that
provide the dialogue between people and the
computer
108.
Rule-based
ES
Knowledge is represented by a series of rules
109.
Frame-based
systems
Knowledge is represented as a series of frames
(an object-oriented approach).
110.
Hybrid
systems
Involve several approaches such as fuzzy logic
and neural networks.
111.
Model-based
systems
Structured around a model that simulates the
structure and function of the system under study.
Ready-made systems Utilize prepackaged
software
112.
Real-time
systems
Systems designed to produce a just-in-time
response
113.
Meta
knowledge
Meta knowledge can be simply defined as
knowledge about knowledge. it use and control
of domain knowledge in an expert system.
114.
MYCIN
MYCIN was the first large expert system to
perform at the level of a human expert and to
provide users with an explanation of its
reasoning
115.
DART
DART uses intelligent agents to aid decision
support system .It integrates a set of intelligent
data processing agent and data base management
system to give planners the ability to rapidly
evaluate plans for logistically feasibility. Dart
decreases the cost and time required to
implement decisions
116.
Domain
Expert
Domain expert--the individual who is considered
an expert.
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117.
Knowledge
engineer
The individual who acquires and represents the
knowledge.
118.
Explanation
facility
The software that answers questions such as
"Why" and “How."
119.
Blackboard
Workplace for storing and working on
intermediate information.
120.
Reasoning
improvement
Facility (not available commercially) for
improving the reasoning capabilities of an ES
121.
Activation
function
In artificial neural networks, the activation
function of a node defines the output of that node
given an input or set of inputs.
122.
Augmented
reality
(AR) is an interactive experience of a real-world
environment where the objects that reside in the
real-world are "augmented" by computer-
generated perceptual information, sometimes
across multiple sensory
modalities,including visual, auditory, haptic, som
atosensory, and olfactory
123.
Backward
chaining
Backward reasoning) is an inference method
described colloquially as working backward
from the goal. It is used in automated theorem
provers, inference engines, proof assistants, and
other artificial intelligenceapplications
124.
Chatbot –
smartbots,
talkbot,
chatterbot,
Bot, IM bot,
interactive agent, Conversational interface or
Artificial Conversational Entity) is a computer
program or an artificial intelligence which
conducts a conversation via auditory or textual
methods
125.
Game theory
The study of mathematical models of strategic
interaction between rational decision-makers
PLACEMENT QUESTIONS
Means-end
analysis
The problem space of means-end analysis has an
initial state and one or more goal states
Cybernetics
Cybernetics is Study of communication between
human and machine.
Traveling
salesman
problem
The traveling salesman problem involves n cities
with paths connecting the cities. The time taken
for traversing through all the cities, without
knowing in advance the length of a minimum
tour, is O(n)
All dogs
have tails
۷x: dog(x) hastail(x).(FOL Representation)
Idempotency
law
Idempotency Law is P V P = P
Utility based
agent
Utility based agent with happy and unhappy state
in artificial intelligence.
Pragmatic
High level knowledge which relates to the use
of sentences in different contexts and how the
context affect the meaning of the sentences
unification,
substitution
The objective of unification procedure is to
discover at least one substitution that causes
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Faculty Team Prepared
Signatures
1.
P.Bhuvaneshwari
HoD
two literals to match
Associative
Network
Directed graph with labelled nodes for
graphical representation of knowledge
Conceptual
Dependencie
s
Primitive concepts and rules to represent
natural language statements
Frames
Frame like structures used to represent
stereotypical patterns for commonly
occurring events in terms of actors, roles,
props and scenes
Script
Knowledge about objects and events is stored
in record-like structures consisting of slots and
slot values.
Supervised
learning
Manual labels of inputs are used
Unsupervise
d learning
Manual labels of inputs are not used
Reinforceme
nt learning
The decision system receives rewards for its
action at the end of a sequence of steps.
Inductive
learning
System learns by example
Turing Test
The imitation game by its creator
Natural
language
processing
Natural language processing can be divided
into the two subfields of generation and
understanding
Strong AI
Strong AI is the embodiment of human
intellectual capabilities within a computer.
human
factors
Investigating the improvement of the
relationship between people and computers
through a technology
IPL
The first AI programming language was called
Simulation
One method of programming a computer to
exhibit human intelligence is called modeling
or simulation
Manipulator
A robot's "arm" is also known as Manipulator
Symbols
One definition of AI focuses on problem-
solving methods that process
ART
Automatic Reasoning Tool is designed to be
used on LISP machines